Embedded Machine Learning

Machine Learning for tiny devices
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Project Overview

It is more and more common to operate under unusual conditions and hard constraints, with unreliable network communication, low power consumption, and the necessity to perform real-time computations. Amethix's team of engineers make the unlikely possible with embedded machine learning and microcontroller (MCU) programming.


Big Brain for Small Devices


tinyML will impact almost every industry in future—retail, healthcare, agriculture, fitness, and manufacturing to name a few.
P. Warden

PROBLEM SPACE

For some of the most innovative organizations operating in multiple sectors, edge computing is not an option but mandatory. Some of the most challenging scenarios require data to be collected, processed, and analyzed on the edge. A plethora of new constraints are raised in such conditions: ultra-low power consumption, hard real-time execution, sharing predictions under very unreliable network conditions. Utilizing microcontrollers powered by small factor batteries represents one of the fewest options at one's disposal. Executing off-the-shelf machine learning models or deep neural networks on such devices becomes practically impossible without custom algorithm design and implementation.

SOLUTION

Amethix develops techniques to compress large machine learning models and optimize their execution on very tiny devices such as SoC and MCU. With the aid of methodologies like model compression, neural network pruning, and quantization, Amethix is capable of building hardware prototypes that are smart and small, preserving most of the accuracy of equivalent machine learning models running in the cloud.


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